PolieDRO: A Novel Classification and Regression Framework with Non-Parametric Data-Driven Regularization
Abstract
PolieDRO is a novel analytics framework for classification and regression that harnesses the power and flexibility of data-driven distributionally robust optimization (DRO) to circumvent the need for regularization hyperparameters. Recent literature shows that traditional machine learning methods such as SVM and (square-root) LASSO can be written as Wasserstein-based DRO problems. Inspired by those results we propose a hyperparameter-free ambiguity set that explores the polyhedral structure of data-driven convex hulls, generating computationally tractable regression and classification methods for any convex loss function. Numerical results based on 100 real-world databases and an extensive experiment with synthetically generated data show that our methods consistently outperform their traditional counterparts.
Cite
Text
Gutierrez et al. "PolieDRO: A Novel Classification and Regression Framework with Non-Parametric Data-Driven Regularization." Machine Learning, 2024. doi:10.1007/S10994-024-06544-9Markdown
[Gutierrez et al. "PolieDRO: A Novel Classification and Regression Framework with Non-Parametric Data-Driven Regularization." Machine Learning, 2024.](https://mlanthology.org/mlj/2024/gutierrez2024mlj-poliedro/) doi:10.1007/S10994-024-06544-9BibTeX
@article{gutierrez2024mlj-poliedro,
title = {{PolieDRO: A Novel Classification and Regression Framework with Non-Parametric Data-Driven Regularization}},
author = {Gutierrez, Tomás and Valladão, Davi Michel and Pagnoncelli, Bernardo K.},
journal = {Machine Learning},
year = {2024},
pages = {5807-5846},
doi = {10.1007/S10994-024-06544-9},
volume = {113},
url = {https://mlanthology.org/mlj/2024/gutierrez2024mlj-poliedro/}
}